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Ernest Valveny; B. Lamiroy |
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Automatic Generation of Browsable Technical Documents. |
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2002 |
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Proceedings of the Sixteenth International Conference on Pattern Recognition ICPR 2002: 188–191. |
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Quebec, Canada |
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DAG @ dag @ VaL2002 |
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301 |
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Josep Llados; Felipe Lumbreras; Javier Varona |
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A multidocument platform for automatic reading of identity cards. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Bilbao |
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ADAS @ adas @ LLV1999 |
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7 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
Recognition of lineal symbols in hand-written drawings using deformable template matching |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes |
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DAG;IAM; |
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IAM @ iam @ VAM1999 |
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1658 |
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Author |
Mohammed Al Rawi; Dimosthenis Karatzas |
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On the Labeling Correctness in Computer Vision Datasets |
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2018 |
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Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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ECML-PKDDW |
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DAG; 600.121; 600.129 |
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Admin @ si @ RaK2018 |
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3144 |
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Dimosthenis Karatzas;Ch. Lioutas |
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Software Package Development for Electron Diffraction Image Analysis |
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1998 |
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Proceedings of the XIV Solid State Physics National Conference |
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Ioannina, Greece |
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IAM @ iam @ KaL1998 |
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2045 |
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Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Multi-class Binary Object Categorization using Blurred Shape Models |
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2007 |
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Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamerican Congress on Pattern |
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4756 |
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773–782 |
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978-3-540-76724-4 |
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CIARP |
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MILAB; DAG;HuPBA |
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BCNPCL @ bcnpcl @ EFP2007 |
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911 |
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Author |
Josep Llados; Ernest Valveny; Enric Marti |
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Symbol Recognition in Document Image Analysis: Methods and Challenges |
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2000 |
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Recent Research Developments in Pattern Recognition, Transworld Research Network, |
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151–178. |
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81-86846-61-1 |
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IAM @ iam @ LVM2000 |
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1575 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
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2016 |
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Recent Trends in Image Processing and Pattern Recognition |
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709 |
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RTIP2R |
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Admin @ si @ HTR2016 |
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2956 |
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Author |
Marçal Rusiñol; Lluis Gomez |
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Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos |
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2018 |
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Revista anual de la Asociación de Archiveros de Castilla y León |
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21 |
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161-174 |
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DAG; 600.121; 600.129 |
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Admin @ si @ RuG2018 |
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3239 |
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Marçal Rusiñol |
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Classificació semàntica i visual de documents digitals |
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2019 |
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Revista de biblioteconomia i documentacio |
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75-86 |
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Se analizan los sistemas de procesamiento automático que trabajan sobre documentos digitalizados con el objetivo de describir los contenidos. De esta forma contribuyen a facilitar el acceso, permitir la indización automática y hacer accesibles los documentos a los motores de búsqueda. El objetivo de estas tecnologías es poder entrenar modelos computacionales que sean capaces de clasificar, agrupar o realizar búsquedas sobre documentos digitales. Así, se describen las tareas de clasificación, agrupamiento y búsqueda. Cuando utilizamos tecnologías de inteligencia artificial en los sistemas de
clasificación esperamos que la herramienta nos devuelva etiquetas semánticas; en sistemas de agrupamiento que nos devuelva documentos agrupados en clusters significativos; y en sistemas de búsqueda esperamos que dada una consulta, nos devuelva una lista ordenada de documentos en función de la relevancia. A continuación se da una visión de conjunto de los métodos que nos permiten describir los documentos digitales, tanto de manera visual (cuál es su apariencia), como a partir de sus contenidos semánticos (de qué hablan). En cuanto a la descripción visual de documentos se aborda el estado de la cuestión de las representaciones numéricas de documentos digitalizados
tanto por métodos clásicos como por métodos basados en el aprendizaje profundo (deep learning). Respecto de la descripción semántica de los contenidos se analizan técnicas como el reconocimiento óptico de caracteres (OCR); el cálculo de estadísticas básicas sobre la aparición de las diferentes palabras en un texto (bag-of-words model); y los métodos basados en aprendizaje profundo como el método word2vec, basado en una red neuronal que, dadas unas cuantas palabras de un texto, debe predecir cuál será la
siguiente palabra. Desde el campo de las ingenierías se están transfiriendo conocimientos que se han integrado en productos o servicios en los ámbitos de la archivística, la biblioteconomía, la documentación y las plataformas de gran consumo, sin embargo los algoritmos deben ser lo suficientemente eficientes no sólo para el reconocimiento y transcripción literal sino también para la capacidad de interpretación de los contenidos. |
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DAG; 600.084; 600.135; 600.121; 600.129 |
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Admin @ si @ Rus2019 |
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3282 |
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